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Dataset results
264 results for “batch”
Macroinvertebrate ring test raw data: batch 2
<p>Raw data from MiSeq runs from labs 2 and 8. includes indexing information for all labs.</p>
Methane production for fed-batch assays amended with graphene oxide and two standard substrates
<p>The spreadsheet contains all the data generated using the Automatic Methane Potential Tests System (AMPTS) for fed-batch experiments containing graphene oxide (GO) at 0, 5, 10, and 20 mg of GO per g of volatile solids (VS).</p> <p>Also, the dataset is divided accordingly to the two substrates used, i.e., glucose (G) and microcrystalline cellulose (C).</p>
F-statistics and ICC measures for testing agreement in DNA methylation across batches, tissues and time
<p>These are the datasets related to the submitted paper: <em>Agreement in DNA methylation levels from the Illumina 450K array, across batches, tissues and time.</em></p> <p>The raw methylation measures are not available due to patient confidentiality. However, we provide the F-statistics and the ICC measures for different subgroupings, and for both datasets. </p>
Figure S1. The MB degradation curves shown are for all the batches of the AgNP samples studied.
Open the record for dataset details and reuse information.
Datatset to article: Batch-to-batch variation in nutrient digestibility of black soldier fly larvae meals in rainbow trout
Open the record for dataset details and reuse information.
Results obained with Image-based Flow Batch
Open the record for dataset details and reuse information.
Supplementary table to "A search for locked ORCIDs reveals a batch of odd accounts"
<p>Supplementary table for: https://doi.org/10.1007/s12262-023-03779-z</p>
Lung CT COVID-19 batch 6
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 4:<a href="http:// https://zenodo.org/record/8043089"> https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p>
Lung CT COVID-19 batch 4
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Lung CT COVID-19 batch 2
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 3: <a href="https://zenodo.org/record/8042817">https://zenodo.org/record/8042817</a></p> <p>Batch 4: <a href="https://zenodo.org/record/8043089">https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Lung CT COVID-19 batch 3
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns COVID-19 RT-PCR outcome prediction and prediction of severe COVID-19, defined as death or intubation after one month, from computed tomography (CT). The data set was previously introduced and described by <a href="https://pubs.rsna.org/doi/full/10.1148/radiol.2021210384">Revel, M. et al (2021)</a>. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The STOIC project collected CT images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This data set contains the training sample of the STOIC dataset as used in the <a href="https://stoic2021.grand-challenge.org/">STOIC2021</a> challenge.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D CT scan</li> </ul> <p>Classification labels:</p> <ul> <li>COVID-19: <ul> <li>0: Negative RT-PCR</li> <li>1: Positive RT-PCR<br> </li> </ul> </li> <li>Severe COVID-19: <ul> <li>0: Alive and no intubation after one month</li> <li>1: Death or intubation after one month</li> </ul> </li> </ul> <p>imagesTr (root folder with all patients and studies)<br> ├── covid19severity_6_0000.mha (3D CT for study 6)<br> ├── covid19severity_17_0000.mha (3D CT for study 17)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://registry.opendata.aws/stoic2021-training/">STOIC2021 training dataset</a>:</p> <pre><code>STOIC2021 Training was accessed on DATE from https://registry.opendata.aws/stoic2021-training. STOIC2021 Training was documented in Thoracic CT in COVID-19: The STOIC Project, Revel, Marie-Pierre, et al. Radiology, 2021, https://doi.org/10.1148/radiol.2021210384.</code></pre> <p>Due to upload size limits, the data set was split into six batches.</p> <p>Batch 1:<a href="https://zenodo.org/record/7969800"> https://zenodo.org/record/7969800</a></p> <p>Batch 2: <a href="https://zenodo.org/record/8042589">https://zenodo.org/record/8042589</a></p> <p>Batch 4: <a href="https://zenodo.org/record/8043089">https://zenodo.org/record/8043089</a></p> <p>Batch 5: <a href="https://zenodo.org/record/8043216">https://zenodo.org/record/8043216</a></p> <p>Batch 6: <a href="https://zenodo.org/record/8043218">https://zenodo.org/record/8043218</a></p>
Java files batch 1 - part 1
<p>Batch #1, part 1 of zip file</p>
Java files batch 1 - part 2
<p>Batch #1, part 2 of zip file</p>
Java files batch 2 - part 1
<p>Batch #2, part 1 of zip file</p>
Java files batch 2 - part 2
<p>Batch #2, part 2 of zip file</p>
HTRU1 Batched Dataset
<p>The <a href="http://www.jb.man.ac.uk/research/ascaife/htru1-batches-py.tar.gz">HTRU1 Batched Dataset</a> is a subset of the HTRU Medlat Training Data, a collection of labeled pulsar candidates from the intermediate galactic latitude part of the HTRU survey. HTRU1 was originally assembled to train the SPINN pulsar classifier. If you use this dataset please cite:</p> <p><em>SPINN: a straightforward machine learning solution to the pulsar candidate selection problem</em> V. Morello, E.D. Barr, M. Bailes, C.M. Flynn, E.F. Keane and W. van Straten, 2014, Monthly Notices of the Royal Astronomical Society, vol. 443, pp. 1651-1662 <a href="http://arxiv.org/abs/1406.3627">arXiv:1406:3627</a></p> <p><em>The High Time Resolution Universe Pulsar Survey - I. System Configuration and Initial Discoveries</em> M. J. Keith et al., 2010, Monthly Notices of the Royal Astronomical Society, vol. 409, pp. 619-627 <a href="https://arxiv.org/abs/1006.5744">arXiv:1006.5744</a></p> <p>The <a href="http://www.jb.man.ac.uk/research/ascaife/htru1-batches-py.tar.gz">HTRU1 Batched Dataset</a> consists of 60000 32x32 images in 2 classes: pulsar & non-pulsar. Each image has 3 channels (equivalent to RGB), but the channels contain different information:</p> <ul> <li><strong>Channel 0:</strong> <em>Period Correction - Dispersion Measure surface</em></li> <li><strong>Channel 1:</strong> <em>Phase - Sub-band surface</em></li> <li><strong>Channel 2:</strong> <em>Phase - Sub-integration surface</em></li> </ul> <p>There are 50000 training images and 10000 test images. The <a href="http://www.jb.man.ac.uk/research/ascaife/htru1-batches-py.tar.gz">HTRU1 Batched Dataset</a> is inspired by the <a href="http://www.cs.toronto.edu/~kriz/cifar.html">CIFAR-10 Dataset</a>.</p> <p>The dataset is divided into five training batches and one test batch. Each batch contains 10000 images. These are in random order, but each batch contains the same balance of pulsar and non-pulsar images. Between them, the six batches contain 1194 true pulsars and 58806 non-pulsars.</p> <p>The <a href="https://raw.githubusercontent.com/as595/HTRU1/master/htru1.py">htru1.py</a> file contains an instance of the <a href="https://pytorch.org/docs/stable/torchvision/datasets.html">torchvision Dataset()</a> for the HTRU1 Batched Dataset.</p>
Comparison of Three Commercial Batches of the Japanese Encephalitis Vaccine IC51
ClinicalTrials.gov study NCT00595465. IPD Sharing: Not stated. Countries: 2. Publications: 0.
Relative Bioavailability of Two Different Batches of a Linagliptin / Metformin Combination Tablet in Healthy Volunteers
ClinicalTrials.gov study NCT01216397. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Relative Bioavailability of Evobrutinib Tablet Batches
ClinicalTrials.gov study NCT07214922. IPD Sharing: NO. Countries: 1. Publications: 0.
Immunogenicity of a Commercial Batch of JEV IC51 up to 24 Months Post Filling
ClinicalTrials.gov study NCT00776230. IPD Sharing: Not stated. Countries: 2. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.